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 | Acesso ao texto completo restrito à biblioteca da Embrapa Semiárido. Para informações adicionais entre em contato com cpatsa.biblioteca@embrapa.br. |
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
23/01/2024 |
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Data da última atualização: |
23/01/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
RAMOS, D. M.; ANDRADE, J. M.; ALBERTON, B. C.; MOURA, M. S. B. de; DOMINGUES, T. F.; NEVES, N.; LIMA, J. R. S.; SOUZA, R.; SOUZA, E.; SILVA, J. R.; SANTO, M. M. E.; MORELLATO, L. P. C.; CUNHA, J. |
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Afiliação: |
DESIRÉE M. RAMOS, Universidade Estadual Paulista - UNESP, São Paulo; JOÃO M. ANDRADE, UFPE; BRUNA C. ALBERTON, Universidade Estadual Paulista - UNESP, São Paulo; MAGNA SOELMA BESERRA DE MOURA, CPATSA; TOMAS F. DOMINGUES, Faculdade de filosofia, Ciência e Letras de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP; NATTÁLIA NEVES, Universidade Estadual Paulista - UNESP, São Paulo; JOSÉ R. S. LIMA, Universidade Federal do Agreste de Pernambuco, Garanhuns, PE; RODOLFO SOUZA, UFRPE - Unidade Acadêmica de Serra Talhada, Serra Talhada, PE; EDUARDO SOUZA, UFRPE - Unidade Acadêmica de Serra Talhada, Serra Talhada, PE; JOSÉ R. SILVA, UFRPE - Unidade Acadêmica de Serra Talhada, Universidade Federal Rural de Pernambuco, Serra Talhada, PE; MÁRIO M. ESPÍRITO-SANTO, Universidade Estadual de Montes Claros, Montes Claros, MG; LEONOR PATRÍCIA CERDEIRA MORELLATO, Instituto de Biociências, Universidade Estadual Paulista - UNESP, São Paulo; JOHN CUNHA, Universidade Federal de Campina Grande. |
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Título: |
Multiscale phenology of seasonally dry tropical forests in an aridity gradient. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Frontiers in Environmental Sciience, v. 11, 1275844, 2023. |
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DOI: |
https://doi.org/10.3389/fenvs.2023.1275844 |
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Idioma: |
Inglês |
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Conteúdo: |
The leaf phenology of seasonally dry tropical forests (SDTFs) is highly seasonal, marked by synchronized flushing of new leaves triggered by the first rains of the wet season. Such phenological transitions may not be accurately detected by remote sensing vegetation indices and derived transition dates (TDs) due to the coarse spatial and temporal resolutions of satellite data. The aim of this study was to compared TDs from PhenoCams and satellite remote sensing (RS) and used the TDs calculated from PhenoCams to select the best thresholds for RS time series and calculate TDs. For this purpose, we assembled cameras in seven sites along an aridity gradient in the Brazilian Caatinga, a region dominated by SDTFs. The leafing patterns were registered during one to three growing seasons from 2017 to 2020. We drew a region of interest (ROI) in the images to calculate the normalized green chromatic coordinate index. We compared the camera data with the NDVI time series (2000–2019) derived from near-infrared (NIR) and red bands from MODIS product data. Using calibrated PhenoCam thresholds reduced the mean absolute error by 5 days for SOS and 34 days for EOS, compared to common thresholds in land surface phenology studies. On average, growing season length (LOS) did not differ significantly among vegetation types, but the driest sites showed the highest interannual variation. This pattern was applied to leaf flushing (SOS) and leaf fall (EOS) as well. We found a positive relationship between the accumulated precipitation and the LOS and between the accumulated precipitation and maximum and minimum temperatures and the vegetation productivity (peak and accumulated NDVI). Our results demonstrated that (A) the fine temporal resolution of phenocamera phenology time series improved the definitions of TDs and thresholds for RS landscape phenology; (b) long-term RS greening responded to the variability in rainfall, adjusting their timing of green-up and green-down, and (C) the amount of rainfall, although not determinant for the length of the growing season, is related to the estimates of vegetation productivity. MenosThe leaf phenology of seasonally dry tropical forests (SDTFs) is highly seasonal, marked by synchronized flushing of new leaves triggered by the first rains of the wet season. Such phenological transitions may not be accurately detected by remote sensing vegetation indices and derived transition dates (TDs) due to the coarse spatial and temporal resolutions of satellite data. The aim of this study was to compared TDs from PhenoCams and satellite remote sensing (RS) and used the TDs calculated from PhenoCams to select the best thresholds for RS time series and calculate TDs. For this purpose, we assembled cameras in seven sites along an aridity gradient in the Brazilian Caatinga, a region dominated by SDTFs. The leafing patterns were registered during one to three growing seasons from 2017 to 2020. We drew a region of interest (ROI) in the images to calculate the normalized green chromatic coordinate index. We compared the camera data with the NDVI time series (2000–2019) derived from near-infrared (NIR) and red bands from MODIS product data. Using calibrated PhenoCam thresholds reduced the mean absolute error by 5 days for SOS and 34 days for EOS, compared to common thresholds in land surface phenology studies. On average, growing season length (LOS) did not differ significantly among vegetation types, but the driest sites showed the highest interannual variation. This pattern was applied to leaf flushing (SOS) and leaf fall (EOS) as well. We found a positive relationship be... Mostrar Tudo |
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Palavras-Chave: |
Análise de séries temporais; Fenologia da superfície terrestre; Imagens PhenoCam; Sensor MODIS. |
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Thesagro: |
Caatinga; Fenologia; Floresta Tropical; Sensoriamento Remoto; Vegetação. |
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Thesaurus Nal: |
Remote sensing; Tropical forests. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 03316naa a2200409 a 4500
001 2161180
005 2024-01-23
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3389/fenvs.2023.1275844$2DOI
100 1 $aRAMOS, D. M.
245 $aMultiscale phenology of seasonally dry tropical forests in an aridity gradient.$h[electronic resource]
260 $c2023
520 $aThe leaf phenology of seasonally dry tropical forests (SDTFs) is highly seasonal, marked by synchronized flushing of new leaves triggered by the first rains of the wet season. Such phenological transitions may not be accurately detected by remote sensing vegetation indices and derived transition dates (TDs) due to the coarse spatial and temporal resolutions of satellite data. The aim of this study was to compared TDs from PhenoCams and satellite remote sensing (RS) and used the TDs calculated from PhenoCams to select the best thresholds for RS time series and calculate TDs. For this purpose, we assembled cameras in seven sites along an aridity gradient in the Brazilian Caatinga, a region dominated by SDTFs. The leafing patterns were registered during one to three growing seasons from 2017 to 2020. We drew a region of interest (ROI) in the images to calculate the normalized green chromatic coordinate index. We compared the camera data with the NDVI time series (2000–2019) derived from near-infrared (NIR) and red bands from MODIS product data. Using calibrated PhenoCam thresholds reduced the mean absolute error by 5 days for SOS and 34 days for EOS, compared to common thresholds in land surface phenology studies. On average, growing season length (LOS) did not differ significantly among vegetation types, but the driest sites showed the highest interannual variation. This pattern was applied to leaf flushing (SOS) and leaf fall (EOS) as well. We found a positive relationship between the accumulated precipitation and the LOS and between the accumulated precipitation and maximum and minimum temperatures and the vegetation productivity (peak and accumulated NDVI). Our results demonstrated that (A) the fine temporal resolution of phenocamera phenology time series improved the definitions of TDs and thresholds for RS landscape phenology; (b) long-term RS greening responded to the variability in rainfall, adjusting their timing of green-up and green-down, and (C) the amount of rainfall, although not determinant for the length of the growing season, is related to the estimates of vegetation productivity.
650 $aRemote sensing
650 $aTropical forests
650 $aCaatinga
650 $aFenologia
650 $aFloresta Tropical
650 $aSensoriamento Remoto
650 $aVegetação
653 $aAnálise de séries temporais
653 $aFenologia da superfície terrestre
653 $aImagens PhenoCam
653 $aSensor MODIS
700 1 $aANDRADE, J. M.
700 1 $aALBERTON, B. C.
700 1 $aMOURA, M. S. B. de
700 1 $aDOMINGUES, T. F.
700 1 $aNEVES, N.
700 1 $aLIMA, J. R. S.
700 1 $aSOUZA, R.
700 1 $aSOUZA, E.
700 1 $aSILVA, J. R.
700 1 $aSANTO, M. M. E.
700 1 $aMORELLATO, L. P. C.
700 1 $aCUNHA, J.
773 $tFrontiers in Environmental Sciience$gv. 11, 1275844, 2023.
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